Correlation Analysis and Analysis of Drought Time Series Based on Modis Satellite Images and Standardized Precipitation Climatic Index (SPI) on the eastern slope of Zagros

Introduction Drought is one of the environmental disasters that is very frequent in arid and semi-arid regions of the country. Rainfall defects have different effects on groundwater, soil moisture, and river flow. Meteorological drought indices are calculated directly from meteorological data such as rainfall and will not be useful in monitoring drought if the data are missing. Therefore remote sensing technique can be a useful tool in drought measurement. Drought is a recognized environmental disaster and has social, economic, and environmental impacts. Shortage of rainfall in a region for long periods of time is known as drought. Drought and rainfall are affecting water and agricultural resources in each region. Materials & Methods The present study is a descriptive-analytic one with emphasis on quantitative methods due to the nature of the problem and the subject under study. In this study, the Tera Sensor Modis satellite images from 2000 and 2017 were used to verify the existence of wet and drought phenomena. In the next step, by examining the rain gauge and synoptic data of the existing stations and using a standardized precipitation index model of three months (May, June and April), the sample was selected. Next, we compared the temperature status indices (TCIs) and vegetation health indices (VHIs) in these three months to determine the differences in these indices over the three months. Modi satellite Tera satellite was used to find out the vegetation status in the study area. Subsequently, using the condition set for the NDVI layer, the vegetation-free areas were separated from the vegetated areas. Experimental method was used to determine the threshold value of this index. For this purpose, different thresholds were tested, with the optimum value of 1 being positive. NDVI is less than 1 plant-free positive and more than vegetation-free. MODIS spectral sensing images for ground surface temperature variables, with a spatial resolution of 1 km, including bands 31 (bandwidth 1080/1180 central bandwidth / 11.017 spatial resolution 1000 m) and 32 bands- 770/11 Central Wavelength Band 032/12 Spatial Resolution Power (1000 m) Selected for months that are almost cloudless. All images have been downloaded from the SearchEarthData site and have been edited. The total rainfall of June, April, and May for the 20-year period was provided by the Meteorological Organization of Iran. ARC GIS software and geostatistical methods were used to process the Excel data. Also, to estimate the correlation between the data Pearson's correlation coefficient was used. Results & Discussion The standardized precipitation index is a powerful tool in analyzing rainfall data. The purpose of this study was to compare the relationship between remote sensing indices and meteorological drought indices and determine the efficiency of remote sensing indices in drought monitoring. Correlation between variables with SPI index was evaluated and calculated. The results of the indicators are different, so a criterion should be used to evaluate the performance of these indicators. SPI index on quarterly time scale (correlated with vegetation) as the preferred criterion Selected. According to the results of correlations, the TCI index with the SPI index had a strong correlation with other indices. In the short run, this index has the highest correlation with thermal indices at 1% level. The correlation between meteorological drought index and plant water content and thermal indices increases with increasing time interval. Positive correlation between vegetation indices and plant water content with meteorological drought indices indicates that trend of changes is in line. Therefore, the TCI index makes drought more accurate and is a better method for estimating drought. Conclusion The results showed that among the surveyed fishes, the highest drought trend was observed in the eastern part of these provinces and covered more than 50% of the area. The trend of changes in this slope was statistically significant. According to the results of correlations, the TCI index with the SPI index had a strong correlation with other indices. It can also be concluded that the Modis images and the processed indices along with the climate indices have the potential for drought monitoring. Using maps derived from drought indices can help improve drought management programs and play a significant role in mitigating drought effects. Keywords Drought, remote sensing, correlation, climate index.